Caring for the radial artery post-angiogram: a pilot study on a comparison of three methods of compression
Bibliographic record
Abstract
BACKGROUND: A coronary angiogram, a diagnostic tool to visualize the coronary anatomy, has traditionally been accessed through the femoral artery. However, in the last 20 years, the radial artery has gained more popularity among physicians and patients, offering an alternative to the femoral approach. Various methods of applying compression to the radial puncture site have been used, but no research has been done to demonstrate the most effective way of achieving hemostasis while limiting complications and ensuring the efficient use of nursing and medical resources. OBJECTIVE: The purpose of this pilot study was to compare two devices and three methods for achieving hemostasis after a transradial angiogram while assessing vascular complications and time endpoints. DESIGN AND METHODS: A mechanical device (Terumo™ wristband) and a hydrophilic wound dressing (Clo-Sur P.A.D.) were used. The Terumo band was studied twice, using the current method and a fast-release method. RESULTS: Taking into account the small sample size of this pilot study (N = 25 per group), statistically significant differences (p ≤ 0.005) are seen in time to discharge in the fast-release Terumo (134.0 minutes) and Clo-Sur P.A.D. groups (113.7 minutes), as compared with the control Terumo group (178.2 minutes), without increasing vascular complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".